dc.contributor.author | Zhang, Jianhua | |
dc.contributor.author | Li, Jianrong | |
dc.contributor.author | Wang, Rubin | |
dc.date.accessioned | 2021-05-26T11:27:42Z | |
dc.date.available | 2021-05-26T11:27:42Z | |
dc.date.created | 2021-03-06T21:06:27Z | |
dc.date.issued | 2020-05-12 | |
dc.identifier.citation | Cognitive Neurodynamics. 2020, 14 (5), 619-642). | en_US |
dc.identifier.issn | 1871-4080 | |
dc.identifier.uri | https://hdl.handle.net/11250/2756435 | |
dc.description.abstract | The real-time assessment of mental workload (MWL) is critical for development of intelligent human–machine cooper- ative systems in various safety–critical applications. Although data-driven machine learning (ML) approach has shown promise in MWL recognition, there is still difficulty in acquiring a sufficient number of labeled data to train the ML models. This paper proposes a semi-supervised extreme learning machine (SS-ELM) algorithm for MWL pattern classi- fication requiring only a small number of labeled data. The measured data analysis results show that the proposed SS-ELM paradigm can effectively improve the accuracy and efficiency of MWL classification and thus provide a competitive ML approach to utilizing a large number of unlabeled data which are available in many real-world applications. | en_US |
dc.description.sponsorship | Open Access funding provided by OsloMet - Oslo Metropolitan University. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Springer | en_US |
dc.relation.ispartofseries | Cognitive Neurodynamics;volume 14, issue 5 | |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.subject | Mental workloads | en_US |
dc.subject | Operator functional states | en_US |
dc.subject | Physiological signals | en_US |
dc.subject | Time–frequency analysis | en_US |
dc.subject | Semi-supervised learning | en_US |
dc.title | Instantaneous mental workload assessment using time–frequency analysis and semi-supervised learning | en_US |
dc.type | Peer reviewed | en_US |
dc.type | Journal article | en_US |
dc.description.version | publishedVersion | en_US |
dc.rights.holder | The Author(s) 2020. | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |
dc.identifier.doi | https://doi.org/10.1007/s11571-020-09589-3 | |
dc.identifier.cristin | 1896117 | |
dc.source.journal | Cognitive Neurodynamics | en_US |
dc.source.volume | 14 | en_US |
dc.source.issue | 5 | en_US |
dc.source.pagenumber | 619-642 | en_US |
dc.relation.project | OsloMet Faculty TKD Lighthouse Project: 201369-100. | en_US |